Ryan is from Kenya, studies finance at the University of Reading, and has traded for about five years (with most of his growth in the last 12 months). He turned $500 into $10,000 gambling NFP, lost it, then moved into prop firm evaluations. With Alpha Capital he reports two performance fees of roughly $300 each from a $25K qualified account, backtests in Python, trades 5-minute moving-average crossovers across gold, indices, and FX, and risks 2.5% on evaluations and 2% on qualified accounts. He is scaling toward $50K, $100K, and $200K accounts next. One trader's story, not a guarantee.
Trader Stats
Name | Ryan |
|---|---|
Age | 21 (at time of interview) |
Origin | Kenya |
Location | UK (studying at University of Reading) |
Study | Finance (quantitative focus) |
Trading since | ~5 years (started during COVID) |
Style | Scalper on 5M charts; MA crossover system |
Instruments | Gold, US30, EUR/USD, GBP/USD, S&P 500 (regime-dependent) |
Sessions | London and New York |
Backtesting | Python (robustness testing, anti-overfitting) |
Risk (evaluations) | Up to 2.5% per trade |
Risk (qualified account) | 2% per trade, fixed |
Alpha Capital account | $25K qualified account (simulated) |
Performance fees (self-reported) | Two requests of roughly $300 each (~$600 total) |
Next goal | Pass a $50K Alpha Capital evaluation, then scale to $100K and $200K |
Ryan sat down with Alpha Capital on his birthday, between lectures and a side job, with a story that mixes early blow-ups, strategy hopping, and a quant finance reset. No mentor. No shortcut. If you are new to how qualified accounts work, read what are funded accounts and trader funding UK before you buy an evaluation.
Kenya, COVID, and fake "traders" online
Ryan did not grow up obsessed with markets. Interest started in Kenya when he met people online who looked like traders.
"Later came to realize they were just cameras. They weren't real traders."
Still, the idea stuck: make money online without a traditional job. COVID gave him time. He moved from stocks into currencies and indices, mostly self-taught on YouTube (including ICT content he folded into his own framework).
No mentor. Ever.
$500 to $10,000 on NFP, then back to zero
Early wins were not skill. They were news gambling.
"I first made $10,000 from $500. I was completely gambling NFP news days every month. Either buy or sell."
He got lucky. Then lost it fast.
"When I went down to my last $500, that's when I decided to invest in prop firms."
He tried several firms over three years before the last year clicked. Total early losses: around $10,000.
His take now:
"I feel like it's better that you lose a lot of money at first than to make a lot of money and then go on to lose it later. Anything that comes easy can go even easier."
The quant reset: stop blaming psychology without an edge
Studying finance at university changed how Ryan thinks about trading.
He realised many traders blame psychology when the real problem is no tested edge. Quant methods gave him a way to build, test, and kill strategies instead of hopping between YouTube setups.
He programs in Python, uses ChatGPT to learn and prototype ideas, and treats backtesting as daily homework, not a one-off project.
"What really ticked for me was when I learned about quantitative finance."
Strategy: regimes, MA crossovers, fundamentals plus technicals
Ryan trades what is moving based on market regime, not one forever pair.
Core technical signal: fast and slow moving average crossovers on the 5-minute chart, filtered for trending conditions he identifies through fundamental work (economic data, Bloomberg, macro drivers like Fed rate expectations, plus LLM tools to stress-test his read).
Entries and exits:
- Technical: crossover signal
- Fundamental: longer-term bias for whether the regime supports the trade
- Stop loss: market structure plus ATR
- Frequency: trades roughly 15 of 20 trading days per month when regime and signals align
He will not give away every parameter (fair). The interview focus is process, not a copy-paste system.
A day in the life: sessions, then Python
Ryan is a scalper, not a swing trader.
Morning / session: London open through New York on the 5M chart.
After ~4:00 PM UK: off the live charts and into backtesting, optimisation, or programming new robustness checks.
That evening block is deliberate psychology:
"It reminds me, oh yeah, I have a good strategy that's profitable."
Python, overfitting, and why manual backtests lied to him
Ryan's biggest lesson from quant work: overfitting is the silent killer.
"A lot of strategies are overfit to past data. You need robustness testing."
He tests whether a strategy holds up under stress, compares returns to S&P 500 benchmarks, and asks whether he is achieving similar returns with lower volatility. That is his definition of edge.
He used to manually backtest on TradingView and MT5. He now calls manual backtesting on TradingView "the worst place" for serious work and points to industry quant culture (he references Jim Simons in the interview) as proof that programmed testing scales better than scrolling bars by hand.
Live trading still requires pushing the button. The data informs decisions. It does not replace them.
Prop firm risk: 2.5% on evals, 2% when qualified
On evaluations, Ryan caps risk at 2.5% per trade.
On qualified accounts, he drops to 2% and keeps risk fixed trade to trade.
Stops are not random pip counts. They follow structure and ATR.
For prop firms specifically, he prefers strategies with a higher win rate so he can pass evaluations and request performance fees without waiting for one rare home-run trade.
"I'm human. I need a good strategy I can stick to long term."
Two performance fees on a $25K account (and drawdown now)
Ryan reports two successful performance fee requests with Alpha Capital, roughly $300 each, from a $25K qualified account. That is about $600 total so far on that size.
He traded $25K accounts for about a year before feeling ready to scale. At interview time he was also in drawdown on his Alpha Capital qualified account. His response: he trusts the plan because he knows which regimes his system wins and loses in.
"If I lose a challenge, I know I'm going to buy another challenge. I have a whole plan around this."
Performance fees are not guaranteed. Past requests are not typical for every trader.
Multiple accounts: manual, segregated risk
Ryan manually trades each account separately. No copy-trader shortcut.
"If I lose an account slowly one by one, I still have another account that can get a performance fee."
That separation keeps psychology cleaner and stops one bad week from wiping every simulated account at once.
All trading income (plus his side job) goes back into new evaluations while he is in student mode. Reinvest first, scale second.
Plan: $25K (consistent) → $50K → $100K → $200K.
Psychology: same routine whether you win or lose
Ryan reads psychology books, trains at the gym, and keeps the same daily routine after a big win or a bad loss.
"If I overreacted to a situation, it would probably leak into my trading."
He treats trading like a business, not a mood swing. Greed and over-conservatism both get checked against the written plan.
Pressure to request performance fees? Real. He does not pretend it disappears.
Biggest early mistake: strategy hopping
Ryan kept a massive notebook of systems: RSI, Bollinger Bands, Wyckoff, and more. Constant switching.
"Strategy hopping is a big issue."
Fix: statistics over attachment. Learn when to keep, tweak, or ditch a system based on testing, not because yesterday felt good on a demo chart.
Advice for new prop firm traders
- Think long term. One hour a day learning beats quitting after one blown evaluation.
- Be honest about greed. You usually know when you are being greedy.
- Learn quant basics. Programming, robust backtests, finance vocabulary. Much of it is learnable free online.
- Practice on demo before you stack evaluation fees.
- Pick a strategy you can actually follow for prop firm rules, not a lottery ticket with a 10% win rate unless you are built for that pain.
- Have energy for the process. Love the work or portion your time so burnout does not kill the edge.
Why Alpha Capital and what is next
Ryan chose to focus on Alpha Capital after years on other firms without the same consistency. His near-term goal is a $50K evaluation because he believes the same process scales once the $25K proof of concept is solid.
"Proof of concept. If it works with the small accounts, surely it works with the big accounts."
He is 21, in final-year energy mode at uni while classmates go out clubbing. Trading changed how fast he matured. Whether that is sustainable is his ongoing balancing act.
Related reads
- What are funded accounts? · Funded forex account guide
- Trader funding UK · Prop trading UK
- Alpha Capital rules explained
- More interviews: Jade De los Santos: $200K qualified · Kim: performance fees and hiding PnL
FAQs
Who is Ryan?
Ryan is a forex and indices trader from Kenya studying finance at the University of Reading. In this Alpha Capital interview he reports two performance fees (~$300 each) from a $25K qualified account. YouTube: Student Trader Shares His Strategy for Long-Term Success.
How much has Ryan made with Alpha Capital?
He reports two performance fee requests of roughly $300 each (~$600 total) from a $25K qualified account. Figures are self-reported, not typical, and not guaranteed.
What strategy does Ryan trade?
5-minute scalping using moving average crossovers, filtered by fundamental regime analysis. He backtests in Python, uses ATR and market structure for stops, and trades gold, US30, EUR/USD, GBP/USD, and S&P 500 when conditions align.
Why does Ryan use Python for backtesting?
To fight overfitting, run robustness tests, and compare strategy performance to benchmarks like the S&P 500. He moved away from manual bar replay on TradingView once his finance degree pushed him toward quant workflow.
How does Ryan manage risk on prop firm accounts?
2.5% max per trade on evaluations, 2% on qualified accounts, fixed sizing, stops from structure + ATR. He manually trades separate accounts so one loss does not wipe every simulated account.
What is Ryan's goal next?
Pass a $50K Alpha Capital evaluation, then scale toward $100K and $200K qualified accounts while reinvesting performance fees and side-job income into new evaluations.
Ready to start your Alpha Capital evaluation?
Ryan's edge is testing, not hype. Demo first. One hour a day beats strategy hopping.
View Alpha Capital evaluation programmes
Alpha Capital Group is a proprietary trading firm based in the United Kingdom. Evaluations and qualified accounts operate in a simulated trading environment with simulated funds. Performance fees are based on eligible simulated trading results; outcomes are not guaranteed. Trader results in this interview reflect individual experiences, not forecasts for future traders. Confirm live rules, eligibility, and pricing on alphacapitalgroup.uk and help.alphacapitalgroup.uk.
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